Unmanned aerial vehicle(UAV)paths in the field directly affect the efficiency and accuracy of payload data collection.Path planning of UAV advancing along river valleys in wild environments is one of the first and mos...Unmanned aerial vehicle(UAV)paths in the field directly affect the efficiency and accuracy of payload data collection.Path planning of UAV advancing along river valleys in wild environments is one of the first and most difficult problems faced by unmanned surveys of debris flow valleys.This study proposes a new hybrid bat optimization algorithm,GRE-Bat(Good point set,Reverse learning,Elite Pool-Bat algorithm),for unmanned exploration path planning of debris flow sources in outdoor environments.In the GRE-Bat algorithm,the good point set strategy is adopted to evenly distribute the population,ensure sufficient coverage of the search space,and improve the stability of the convergence accuracy of the algorithm.Subsequently,a reverse learning strategy is introduced to increase the diversity of the population and improve the local stagnation problem of the algorithm.In addition,an Elite pool strategy is added to balance the replacement and learning behaviors of particles within the population based on elimination and local perturbation factors.To demonstrate the effectiveness of the GRE-Bat algorithm,we conducted multiple simulation experiments using benchmark test functions and digital terrain models.Compared to commonly used path planning algorithms such as the Bat Algorithm(BA)and the Improved Sparrow Search Algorithm(ISSA),the GRE-Bat algorithm can converge to the optimal value in different types of test functions and obtains a near-optimal solution after an average of 60 iterations.The GRE-Bat algorithm can obtain higher quality flight routes in the designated environment of unmanned investigation in the debris flow gully basin,demonstrating its potential for practical application.展开更多
As an essential tool for realistic description of the current or future debris environment,the Space Debris Environment Engineering Model(SDEEM)has been developed to provide support for risk assessment of spacecraft.I...As an essential tool for realistic description of the current or future debris environment,the Space Debris Environment Engineering Model(SDEEM)has been developed to provide support for risk assessment of spacecraft.In contrast with SDEEM2015,SDEEM2019,the latest version,extends the orbital range from the Low Earth Orbit(LEO)to Geosynchronous Orbit(GEO)for the years 1958-2050.In this paper,improved modeling algorithms used by SDEEM2019 in propagating simulation,spatial density distribution,and spacecraft flux evaluation are presented.The debris fluxes of SDEEM2019 are compared with those of three typical models,i.e.,SDEEM2015,Orbital Debris Engineering Model 3.1(ORDEM 3.1),and Meteoroid and Space Debris Terrestrial Environment Reference(MASTER-8),in terms of two assessment modes.Three orbital cases,including the Geostationary Transfer Orbit(GTO),Sun-Synchronous Orbit(SSO)and International Space Station(ISS)orbit,are selected for the spacecraft assessment mode,and the LEO region is selected for the spatial density assessment mode.The analysis indicates that compared with previous algorithms,the variable step-size orbital propagating algorithm based on semi-major axis control is more precise,the spatial density algorithm based on the second zonal harmonic of the non-spherical Earth gravity(J_(2))is more applicable,and the result of the position-centered spacecraft flux algorithm is more convergent.The comparison shows that SDEEM2019 and MASTER-8 have consistent trends due to similar modeling processes,while the differences between SDEEM2019 and ORDEM 3.1 are mainly caused by different modeling approaches for uncatalogued debris.展开更多
Estimation of velocity profile within mud depth is a long-standing and essential problem in debris flow dynamics.Until now,various velocity profiles have been proposed based on the fitting analysis of experimental mea...Estimation of velocity profile within mud depth is a long-standing and essential problem in debris flow dynamics.Until now,various velocity profiles have been proposed based on the fitting analysis of experimental measurements,but these are often limited by the observation conditions,such as the number of configured sensors.Therefore,the resulting linear velocity profiles usually exhibit limitations in reproducing the temporal-varied and nonlinear behavior during the debris flow process.In this study,we present a novel approach to explore the debris flow velocity profile in detail upon our previous 3D-HBPSPH numerical model,i.e.,the three-dimensional Smoothed Particle Hydrodynamic model incorporating the Herschel-Bulkley-Papanastasiou rheology.Specifically,we propose a stratification aggregation algorithm for interpreting the details of SPH particles,which enables the recording of temporal velocities of debris flow at different mud depths.To analyze the velocity profile,we introduce a logarithmic-based nonlinear model with two key parameters,that a controlling the shape of velocity profile and b concerning its temporal evolution.We verify the proposed velocity profile and explore its sensitivity using 34 sets of velocity data from three individual flume experiments in previous literature.Our results demonstrate that the proposed temporalvaried nonlinear velocity profile outperforms the previous linear profiles.展开更多
The behind-armor debris(BAD) formed by the perforation of an EFP is the main damage factor for the secondary destruction to the behind-armor components.Aiming at investigating the BAD caused by EFP,flash X-ray radiogr...The behind-armor debris(BAD) formed by the perforation of an EFP is the main damage factor for the secondary destruction to the behind-armor components.Aiming at investigating the BAD caused by EFP,flash X-ray radiography combined with an experimental witness plate test method was used,and the FEM-SPH adaptive conversion algorithm in LS-DYNA software was erployed to model the perforation process.The simulation results of the debris cloud shape and number of debris were in good agreement with the flash X-ray radiographs and perforated holes on the witness plate,respectlvely.Threedimensional numerical simulations of EFP's penetration under various impact conditions were conducted.The results show that,an ellipsoidal debris cloud,with the major-to-minor axis radio(a/b)smaller than that caused by shaped charge jets,was formed behind the target.With the increase of target thickness(h) and decrease of impact velocity(v_0) and obliquity(θ),the value of a/b decreases.The number of debris ejected from target is significantly higher than that from EFP.Based on the statistical analysis of the spatial distribution of the BAD,An engineering calculation model was established considering the influence of h,v_0 and θ.The model can with reasonable accuracy predict the quantity and velocity distribution characteristics of BAD formed by EFP.展开更多
In this paper,a new mission model,called a multi-debris active removal mission with partial debris capture strategy,is proposed.The model assumes that a platform only captures part of the scheduled debris at a time an...In this paper,a new mission model,called a multi-debris active removal mission with partial debris capture strategy,is proposed.The model assumes that a platform only captures part of the scheduled debris at a time and then releases these debris pieces to a disposal orbit.This process is then repeated until all of the scheduled debris is removed.A genetic algorithm with a multiparameter concatenated coding method is designed to optimize the plan of a multi-debris active removal mission with a partial debris capture strategy.A set of six pieces of debris and a set of 10 pieces of debris are selected to demonstrate the proposed planning method.The result confirms the effectiveness of the genetic algorithm with the multi-parameter concatenated coding method.The new mission model provides a more comprehensive decision-making framework than the existing mission models and makes it possible to further decrease mission costs.展开更多
Debris flows are recurrent natural hazards in many mountainous regions.This paper presents a numerical study on the propagation of debris flows in natural erodible open channels,in which the bed erosion and sedimentat...Debris flows are recurrent natural hazards in many mountainous regions.This paper presents a numerical study on the propagation of debris flows in natural erodible open channels,in which the bed erosion and sedimentation processes are important.Based on the Bingham fluid theory,a mathematical model of the two-dimensional non-constant debris flow is developed.The governing equations include the continuity and momentum conservation equations of debris flow,the sediment convection-diffusion equation,the bed erosion-deposition equation and the bed-sediment size gradation adjustment equation.The yield stress and shear stress components are included to describe the dynamic rheological properties.The upwind control-volume Finite Volume Method (FVM) is applied to discretize the convection terms.The improved SIMPLE algorithm with velocity-free-surface coupled correction is developed to solve the equations on non-orthogonal,quadrilateral grids.The model is applied to simulate a debris flow event in Jiangjia Gully,Yunnan Province and to predict the flow pattern and bed erosion-deposition processes.The results show the effectiveness of the proposed numercial model in debris flow simulation and potential hazard analysis.展开更多
Recent studies of the space debris environment in Low Earth Orbit(LEO)have shown that the critical density of space debris has been reached in certain regions.The Active Debris Removal(ADR)mission,to mitigate the spac...Recent studies of the space debris environment in Low Earth Orbit(LEO)have shown that the critical density of space debris has been reached in certain regions.The Active Debris Removal(ADR)mission,to mitigate the space debris density and stabilize the space debris environment,has been considered as a most effective method.In this paper,a novel two-level optimization strategy for multi-debris removal mission in LEO is proposed,which includes the low-level and high-level optimization process.To improve the overall performance of the multi-debris active removal mission and obtain multiple Pareto-optimal solutions,the ADR mission is seen as a Time-Dependant Traveling Salesman Problem(TDTSP)with two objective functions to minimize the total mission duration and the total propellant consumption.The problem includes the sequence optimization to determine the sequence of removal of space debris and the transferring optimization to define the orbital maneuvers.Two optimization models for the two-level optimization strategy are built in solving the multi-debris removal mission,and the optimal Pareto solution is successfully obtained by using the non-dominated sorting genetic algorithm II(NSGA-II).Two test cases are presented,which show that the low level optimization strategy can successfully obtain the optimal sequences and the initial solution of the ADR mission and the high level optimization strategy can efficiently and robustly find the feasible optimal solution for long duration perturbed rendezvous problem.展开更多
The primary purpose of this study is to exploit the effect of Earth＇s non-sphericity perturbation, particularly due to the J2 term, in order to optimize the capture sequence of potential orbital debris, that is the c...The primary purpose of this study is to exploit the effect of Earth＇s non-sphericity perturbation, particularly due to the J2 term, in order to optimize the capture sequence of potential orbital debris, that is the cumulative AV associated to the transfers between one object and the others. As results of several researches and model predictions, many international agencies agree that the growing population of objects and debris in LEO （low earth orbits）, will follow a diverging trend in the future. This, in turn, would constitute a serious threat to circum-terrestrial space safety and sustainability. In LEO, the ,J disturbance is prevailing over the others, and it acts by affecting the longitude of the ascending node （Ω）, the argument of perigee （ω） and, accordingly, the true anomaly （v）. Therefore, the goal of optimizing the AV is achieved by taking advantage of the rate of variation of Ω and ω, thereby compensating for the △Ω and △ω, present between the orbital transfer vehicle （chaser） and the debris to be captured （target）. Obviously, the perturbation will lead to favourable variations of the orbital parameters only for some combinations of Ω and ω. Yet the presence of a debris population with random distribution of Ω and ω, makes this application particularly suited to the problem. The single maneuver has been modelled with a 4-impulse time fixed rendezvous and the optimization problem has been addressed by implementing a hybrid evolutionary algorithm, which adopts, in parallel, three different strategies, namely, genetic algorithm, differential evolution and particle swarm optimization.展开更多
Infrasound,known for its strong penetration and low attenuation,is extensively used in monitoring and warning systems for debris flows.Here,a debris-flow forecasting method was proposed by combining infrasound-based v...Infrasound,known for its strong penetration and low attenuation,is extensively used in monitoring and warning systems for debris flows.Here,a debris-flow forecasting method was proposed by combining infrasound-based variational mode decomposition and Autoregressive Integrated Moving Average(ARIMA)model.High-precision infrasound sensor was utilized in experiments to record signals under twelve varying conditions of debris flow volume and velocity.Variational mode decomposition was performed on the detected raw signals,and the optimal decomposition scale and penalty factor were obtained through the sparrow search algorithm.The Hilbert transform,rescaled range analysis,power spectrum analysis,and Pearson correlation coefficients judgment criteria were employed to separate and reconstruct the signals.Based on the reconstructed infrasound signals,an ARIMA model was constructed to forecast the trend of debris flow infrasound signal.Results reveal that the Hilbert transform effectively separated noise,and the predictive model’s results fell within a 95%confidence interval.The Mean Absolute Percentage Error(MAPE)across four experiments were 4.87%,5.23%,5.32%and 4.47%,respectively,showing a satisfactory accuracy and providing an alternative for predicting debris flow by infrasound signals.展开更多
针对多级火箭残骸定位精度不足的问题,提出一种融合粒子群算法与牛顿迭代-梯度下降法的时间差定位方法(Time Difference of Arrival,TDOA)联合优化模型,通过全局搜索与局部优化的协同机制提升定位性能,并构建多残骸信号分离约束模型与...针对多级火箭残骸定位精度不足的问题,提出一种融合粒子群算法与牛顿迭代-梯度下降法的时间差定位方法(Time Difference of Arrival,TDOA)联合优化模型,通过全局搜索与局部优化的协同机制提升定位性能,并构建多残骸信号分离约束模型与环境干扰补偿模型。试验表明,该模型在火箭残骸回收任务中,定位误差由传统单级优化算法的1~10 km降低至0.5 km以内,多残骸信号分离率达96.2%,山地及强风干扰下仍保持亚千米级精度。结合Chan-Taylor算法与最小二乘法的验证表明,其抗干扰性与定位可靠性显著优于现有方法。本算法可拓展至移动通信、无人驾驶等领域,兼具理论创新与工程应用价值。展开更多
This study investigated the impacts of random negative training datasets(NTDs)on the uncertainty of machine learning models for geologic hazard susceptibility assessment of the Loess Plateau,northern Shaanxi Province,...This study investigated the impacts of random negative training datasets(NTDs)on the uncertainty of machine learning models for geologic hazard susceptibility assessment of the Loess Plateau,northern Shaanxi Province,China.Based on randomly generated 40 NTDs,the study developed models for the geologic hazard susceptibility assessment using the random forest algorithm and evaluated their performances using the area under the receiver operating characteristic curve(AUC).Specifically,the means and standard deviations of the AUC values from all models were then utilized to assess the overall spatial correlation between the conditioning factors and the susceptibility assessment,as well as the uncertainty introduced by the NTDs.A risk and return methodology was thus employed to quantify and mitigate the uncertainty,with log odds ratios used to characterize the susceptibility assessment levels.The risk and return values were calculated based on the standard deviations and means of the log odds ratios of various locations.After the mean log odds ratios were converted into probability values,the final susceptibility map was plotted,which accounts for the uncertainty induced by random NTDs.The results indicate that the AUC values of the models ranged from 0.810 to 0.963,with an average of 0.852 and a standard deviation of 0.035,indicating encouraging prediction effects and certain uncertainty.The risk and return analysis reveals that low-risk and high-return areas suggest lower standard deviations and higher means across multiple model-derived assessments.Overall,this study introduces a new framework for quantifying the uncertainty of multiple training and evaluation models,aimed at improving their robustness and reliability.Additionally,by identifying low-risk and high-return areas,resource allocation for geologic hazard prevention and control can be optimized,thus ensuring that limited resources are directed toward the most effective prevention and control measures.展开更多
基金supported by National Natural Science Foundation of China(No.42302336)Project of the Department of Science and Technology of Sichuan Province(No.2024YFHZ0098,No.2023NSFSC0751)Open Project of Chengdu University of Information Technology(KYQN202317,760115027,KYTZ202278,KYTZ202280).
文摘Unmanned aerial vehicle(UAV)paths in the field directly affect the efficiency and accuracy of payload data collection.Path planning of UAV advancing along river valleys in wild environments is one of the first and most difficult problems faced by unmanned surveys of debris flow valleys.This study proposes a new hybrid bat optimization algorithm,GRE-Bat(Good point set,Reverse learning,Elite Pool-Bat algorithm),for unmanned exploration path planning of debris flow sources in outdoor environments.In the GRE-Bat algorithm,the good point set strategy is adopted to evenly distribute the population,ensure sufficient coverage of the search space,and improve the stability of the convergence accuracy of the algorithm.Subsequently,a reverse learning strategy is introduced to increase the diversity of the population and improve the local stagnation problem of the algorithm.In addition,an Elite pool strategy is added to balance the replacement and learning behaviors of particles within the population based on elimination and local perturbation factors.To demonstrate the effectiveness of the GRE-Bat algorithm,we conducted multiple simulation experiments using benchmark test functions and digital terrain models.Compared to commonly used path planning algorithms such as the Bat Algorithm(BA)and the Improved Sparrow Search Algorithm(ISSA),the GRE-Bat algorithm can converge to the optimal value in different types of test functions and obtains a near-optimal solution after an average of 60 iterations.The GRE-Bat algorithm can obtain higher quality flight routes in the designated environment of unmanned investigation in the debris flow gully basin,demonstrating its potential for practical application.
文摘As an essential tool for realistic description of the current or future debris environment,the Space Debris Environment Engineering Model(SDEEM)has been developed to provide support for risk assessment of spacecraft.In contrast with SDEEM2015,SDEEM2019,the latest version,extends the orbital range from the Low Earth Orbit(LEO)to Geosynchronous Orbit(GEO)for the years 1958-2050.In this paper,improved modeling algorithms used by SDEEM2019 in propagating simulation,spatial density distribution,and spacecraft flux evaluation are presented.The debris fluxes of SDEEM2019 are compared with those of three typical models,i.e.,SDEEM2015,Orbital Debris Engineering Model 3.1(ORDEM 3.1),and Meteoroid and Space Debris Terrestrial Environment Reference(MASTER-8),in terms of two assessment modes.Three orbital cases,including the Geostationary Transfer Orbit(GTO),Sun-Synchronous Orbit(SSO)and International Space Station(ISS)orbit,are selected for the spacecraft assessment mode,and the LEO region is selected for the spatial density assessment mode.The analysis indicates that compared with previous algorithms,the variable step-size orbital propagating algorithm based on semi-major axis control is more precise,the spatial density algorithm based on the second zonal harmonic of the non-spherical Earth gravity(J_(2))is more applicable,and the result of the position-centered spacecraft flux algorithm is more convergent.The comparison shows that SDEEM2019 and MASTER-8 have consistent trends due to similar modeling processes,while the differences between SDEEM2019 and ORDEM 3.1 are mainly caused by different modeling approaches for uncatalogued debris.
基金supported by the National Natural Science Foundation of China(Grant No.52078493)the Natural Science Foundation of Hunan Province(Grant No.2022JJ30700)+2 种基金the Natural Science Foundation for Excellent Young Scholars of Hunan(Grant No.2021JJ20057)the Science and Technology Plan Project of Changsha(Grant No.kq2305006)the Innovation Driven Program of Central South University(Grant No.2023CXQD033).
文摘Estimation of velocity profile within mud depth is a long-standing and essential problem in debris flow dynamics.Until now,various velocity profiles have been proposed based on the fitting analysis of experimental measurements,but these are often limited by the observation conditions,such as the number of configured sensors.Therefore,the resulting linear velocity profiles usually exhibit limitations in reproducing the temporal-varied and nonlinear behavior during the debris flow process.In this study,we present a novel approach to explore the debris flow velocity profile in detail upon our previous 3D-HBPSPH numerical model,i.e.,the three-dimensional Smoothed Particle Hydrodynamic model incorporating the Herschel-Bulkley-Papanastasiou rheology.Specifically,we propose a stratification aggregation algorithm for interpreting the details of SPH particles,which enables the recording of temporal velocities of debris flow at different mud depths.To analyze the velocity profile,we introduce a logarithmic-based nonlinear model with two key parameters,that a controlling the shape of velocity profile and b concerning its temporal evolution.We verify the proposed velocity profile and explore its sensitivity using 34 sets of velocity data from three individual flume experiments in previous literature.Our results demonstrate that the proposed temporalvaried nonlinear velocity profile outperforms the previous linear profiles.
基金supported by National Natural Science Foundation of China(Grant no.11872123)。
文摘The behind-armor debris(BAD) formed by the perforation of an EFP is the main damage factor for the secondary destruction to the behind-armor components.Aiming at investigating the BAD caused by EFP,flash X-ray radiography combined with an experimental witness plate test method was used,and the FEM-SPH adaptive conversion algorithm in LS-DYNA software was erployed to model the perforation process.The simulation results of the debris cloud shape and number of debris were in good agreement with the flash X-ray radiographs and perforated holes on the witness plate,respectlvely.Threedimensional numerical simulations of EFP's penetration under various impact conditions were conducted.The results show that,an ellipsoidal debris cloud,with the major-to-minor axis radio(a/b)smaller than that caused by shaped charge jets,was formed behind the target.With the increase of target thickness(h) and decrease of impact velocity(v_0) and obliquity(θ),the value of a/b decreases.The number of debris ejected from target is significantly higher than that from EFP.Based on the statistical analysis of the spatial distribution of the BAD,An engineering calculation model was established considering the influence of h,v_0 and θ.The model can with reasonable accuracy predict the quantity and velocity distribution characteristics of BAD formed by EFP.
基金co-supported by the Open Fund Project of Space Intelligent Control Technology Laboratory(No.HTKJ2021KL502010)the Research Project of Space Debris and Near-earth Asteroid Defense Grants,China(No.KJSP 2020010303)the National Natural Science Foundation of China(No.11802130).
文摘In this paper,a new mission model,called a multi-debris active removal mission with partial debris capture strategy,is proposed.The model assumes that a platform only captures part of the scheduled debris at a time and then releases these debris pieces to a disposal orbit.This process is then repeated until all of the scheduled debris is removed.A genetic algorithm with a multiparameter concatenated coding method is designed to optimize the plan of a multi-debris active removal mission with a partial debris capture strategy.A set of six pieces of debris and a set of 10 pieces of debris are selected to demonstrate the proposed planning method.The result confirms the effectiveness of the genetic algorithm with the multi-parameter concatenated coding method.The new mission model provides a more comprehensive decision-making framework than the existing mission models and makes it possible to further decrease mission costs.
基金supported by the National Basic Research Program of China (973 Program)(Grant No.2011CB409902)the Knowledge Innovation Project of the Chinese Academy of Sciences (No.KZCX2-YW-302)
文摘Debris flows are recurrent natural hazards in many mountainous regions.This paper presents a numerical study on the propagation of debris flows in natural erodible open channels,in which the bed erosion and sedimentation processes are important.Based on the Bingham fluid theory,a mathematical model of the two-dimensional non-constant debris flow is developed.The governing equations include the continuity and momentum conservation equations of debris flow,the sediment convection-diffusion equation,the bed erosion-deposition equation and the bed-sediment size gradation adjustment equation.The yield stress and shear stress components are included to describe the dynamic rheological properties.The upwind control-volume Finite Volume Method (FVM) is applied to discretize the convection terms.The improved SIMPLE algorithm with velocity-free-surface coupled correction is developed to solve the equations on non-orthogonal,quadrilateral grids.The model is applied to simulate a debris flow event in Jiangjia Gully,Yunnan Province and to predict the flow pattern and bed erosion-deposition processes.The results show the effectiveness of the proposed numercial model in debris flow simulation and potential hazard analysis.
基金the Open Research Foundation of Science and Technology in Aerospace Flight Dynamics Laboratory of China(GF2018005).
文摘Recent studies of the space debris environment in Low Earth Orbit(LEO)have shown that the critical density of space debris has been reached in certain regions.The Active Debris Removal(ADR)mission,to mitigate the space debris density and stabilize the space debris environment,has been considered as a most effective method.In this paper,a novel two-level optimization strategy for multi-debris removal mission in LEO is proposed,which includes the low-level and high-level optimization process.To improve the overall performance of the multi-debris active removal mission and obtain multiple Pareto-optimal solutions,the ADR mission is seen as a Time-Dependant Traveling Salesman Problem(TDTSP)with two objective functions to minimize the total mission duration and the total propellant consumption.The problem includes the sequence optimization to determine the sequence of removal of space debris and the transferring optimization to define the orbital maneuvers.Two optimization models for the two-level optimization strategy are built in solving the multi-debris removal mission,and the optimal Pareto solution is successfully obtained by using the non-dominated sorting genetic algorithm II(NSGA-II).Two test cases are presented,which show that the low level optimization strategy can successfully obtain the optimal sequences and the initial solution of the ADR mission and the high level optimization strategy can efficiently and robustly find the feasible optimal solution for long duration perturbed rendezvous problem.
文摘The primary purpose of this study is to exploit the effect of Earth＇s non-sphericity perturbation, particularly due to the J2 term, in order to optimize the capture sequence of potential orbital debris, that is the cumulative AV associated to the transfers between one object and the others. As results of several researches and model predictions, many international agencies agree that the growing population of objects and debris in LEO （low earth orbits）, will follow a diverging trend in the future. This, in turn, would constitute a serious threat to circum-terrestrial space safety and sustainability. In LEO, the ,J disturbance is prevailing over the others, and it acts by affecting the longitude of the ascending node （Ω）, the argument of perigee （ω） and, accordingly, the true anomaly （v）. Therefore, the goal of optimizing the AV is achieved by taking advantage of the rate of variation of Ω and ω, thereby compensating for the △Ω and △ω, present between the orbital transfer vehicle （chaser） and the debris to be captured （target）. Obviously, the perturbation will lead to favourable variations of the orbital parameters only for some combinations of Ω and ω. Yet the presence of a debris population with random distribution of Ω and ω, makes this application particularly suited to the problem. The single maneuver has been modelled with a 4-impulse time fixed rendezvous and the optimization problem has been addressed by implementing a hybrid evolutionary algorithm, which adopts, in parallel, three different strategies, namely, genetic algorithm, differential evolution and particle swarm optimization.
基金funded by National Key R&D Program of China(No.2022YFC3003403)Sichuan Science and Technology Program(No.2024NSFSC0072)+1 种基金Natural Science Foundation of Hebei Province(No.F2021201031)Geological Survey Project of China Geological Survey(No.DD20230442).
文摘Infrasound,known for its strong penetration and low attenuation,is extensively used in monitoring and warning systems for debris flows.Here,a debris-flow forecasting method was proposed by combining infrasound-based variational mode decomposition and Autoregressive Integrated Moving Average(ARIMA)model.High-precision infrasound sensor was utilized in experiments to record signals under twelve varying conditions of debris flow volume and velocity.Variational mode decomposition was performed on the detected raw signals,and the optimal decomposition scale and penalty factor were obtained through the sparrow search algorithm.The Hilbert transform,rescaled range analysis,power spectrum analysis,and Pearson correlation coefficients judgment criteria were employed to separate and reconstruct the signals.Based on the reconstructed infrasound signals,an ARIMA model was constructed to forecast the trend of debris flow infrasound signal.Results reveal that the Hilbert transform effectively separated noise,and the predictive model’s results fell within a 95%confidence interval.The Mean Absolute Percentage Error(MAPE)across four experiments were 4.87%,5.23%,5.32%and 4.47%,respectively,showing a satisfactory accuracy and providing an alternative for predicting debris flow by infrasound signals.
基金supported by a project entitled Loess Plateau Region-Watershed-Slope Geological Hazard Multi-Scale Collaborative Intelligent Early Warning System of the National Key R&D Program of China(2022YFC3003404)a project of the Shaanxi Youth Science and Technology Star(2021KJXX-87)public welfare geological survey projects of Shaanxi Institute of Geologic Survey(20180301,201918,202103,and 202413).
文摘This study investigated the impacts of random negative training datasets(NTDs)on the uncertainty of machine learning models for geologic hazard susceptibility assessment of the Loess Plateau,northern Shaanxi Province,China.Based on randomly generated 40 NTDs,the study developed models for the geologic hazard susceptibility assessment using the random forest algorithm and evaluated their performances using the area under the receiver operating characteristic curve(AUC).Specifically,the means and standard deviations of the AUC values from all models were then utilized to assess the overall spatial correlation between the conditioning factors and the susceptibility assessment,as well as the uncertainty introduced by the NTDs.A risk and return methodology was thus employed to quantify and mitigate the uncertainty,with log odds ratios used to characterize the susceptibility assessment levels.The risk and return values were calculated based on the standard deviations and means of the log odds ratios of various locations.After the mean log odds ratios were converted into probability values,the final susceptibility map was plotted,which accounts for the uncertainty induced by random NTDs.The results indicate that the AUC values of the models ranged from 0.810 to 0.963,with an average of 0.852 and a standard deviation of 0.035,indicating encouraging prediction effects and certain uncertainty.The risk and return analysis reveals that low-risk and high-return areas suggest lower standard deviations and higher means across multiple model-derived assessments.Overall,this study introduces a new framework for quantifying the uncertainty of multiple training and evaluation models,aimed at improving their robustness and reliability.Additionally,by identifying low-risk and high-return areas,resource allocation for geologic hazard prevention and control can be optimized,thus ensuring that limited resources are directed toward the most effective prevention and control measures.
